SoroushVahidi/scidocs huggingface dataset:
Photovoltaic Power Conditioning System With Line Connection
(2006)

A photovoltaic (PV) power conditioning system (PCS) with line connection is proposed. Using the power slope versus voltage of the PV array, the maximum power point tracking (MPPT) controller that produces a smooth transition to the maximum power point is proposed. The dc current of the PV array is estimated without using a dc current sensor. A current controller is suggested to provide power to the line with an almost-unity power factor that is derived using the feedback linearization concept. The disturbance of the line voltage is detected using a fast sensing technique. All control functions are implemented in software with a single-chip microcontroller. Experimental results obtained on a 2-kW prototype show high performance such as an almost-unity power factor, a power efficiency of 94%, and a total harmonic distortion (THD) of 3.6%
Predicting Movie Revenue from IMDb Data
(2011)

Given the information known about a movie in the we ek of its release, can we predict the total gross revenue for that movie? Such information would be u s f l to marketers, theater operators, and others i n the movie industry, but it is a hard problem, even for human beings. We found that, given a set of numeric , textbased, and sentiment features from IMDb, linear reg ession outperforms class-based logistic regression at predicting gross revenue. However, neither gives su fficiently precise results to be used in practice.
Semi-productive Polysemy and Sense Extension
(1995)

In this paper we discuss various aspects of systematic or conventional polysemy and their formal treatment within an implemented constraint based approach to linguistic representation. We distinguish between two classes of systematic polysemy: constructional polysemy, where a single sense assigned to a lexical entry is contextually specialised, and sense extension, which predictably relates two or more senses. Formally the first case is treated as instantiation of an underspecified lexical entry and the second by use of lexical rules. The problems of distinguishing between these two classes are discussed in detail. We illustrate how lexical rules can be used both to relate fully conventionalised senses and also applied productively to recognise novel usages and how this process can be controlled to account for semi-productivity by utilising probabilities.
Type shifting in construction grammar : An integrated approach to aspectual coercion *
(2004)

Implicit type shifting, or coercion, appears to indicate a modular grammatical architecture, in which the process of semantic composition may add meanings absent from the syntax in order to ensure that certain operators, e.g., the progressive, receive suitable arguments (Jackendo¤ 1997; De Swart 1998). I will argue that coercion phenomena actually provide strong support for a sign-based model of grammar, in which rules of morphosyntactic combination can shift the designations of content words with which they combine. On this account, enriched composition is a by-product of the ordinary referring behavior of constructions. Thus, for example, the constraint which requires semantic concord between the syntactic sisters in the string a bottle is also what underlies the coerced interpretation found in a beer. If this concord constraint is stated for a rule of morphosyntactic combination, we capture an important generalization: a single combinatory mechanism, the construction, is responsible for both coerced and compositional meanings. Since both type-selecting constructions (e.g., the French Imparfait) and type-shifting constructions (e.g., English progressive aspect) require semantic concord between syntactic sisters, we account for the fact that constructions of both types perform coercion. Coercion data suggest that aspectual sensitivity is not merely a property of formally di¤erentiated past tenses, as in French and Latin, but a general property of tense constructions, including the English present and past tenses.
Grammatical Constructions and Linguistic Generalizations : the What ' s X doing Y ? Construction 1
(1997)

To adopt a constructional approach is to undertake a commitment in principle to account for the entirety of each language. This means that the relatively general patterns of the language, such as the one licensing the ordering of a finite auxiliary verb before its subject in English as illustrated in (1), and the more idiomatic patterns, such as those exemplified in (2), stand on an equal footing as data for which the grammar must provide an account.
On the relationship between math anxiety and math achievement in early elementary school: The role of problem solving strategies.
(2016)

Even at young ages, children self-report experiencing math anxiety, which negatively relates to their math achievement. Leveraging a large dataset of first and second grade students' math achievement scores, math problem solving strategies, and math attitudes, we explored the possibility that children's math anxiety (i.e., a fear or apprehension about math) negatively relates to their use of more advanced problem solving strategies, which in turn relates to their math achievement. Our results confirm our hypothesis and, moreover, demonstrate that the relation between math anxiety and math problem solving strategies is strongest in children with the highest working memory capacity. Ironically, children who have the highest cognitive capacity avoid using advanced problem solving strategies when they are high in math anxiety and, as a result, underperform in math compared with their lower working memory peers.
Selective harmonic elimination modulation technique applied on four-leg NPC
(2014)

In this paper the selective harmonic elimination pulse width modulation (SHE-PWM) technique is proposed to control the three-level four-leg neutral point clamped (NPC) inverter in order to have both advantages of low switching frequency of SHE and neutral point of fourth leg in four wire systems. In this proposed method, the obtained switching angles of inverter phase legs are used to eliminate the non-triplen, 5th to 23th harmonics orders from the output voltage. As well, switching angles calculated for the fourth leg are considered to eliminate the triplen harmonics containing 3th, 9th, 15th, 21th and 27th orders from phase voltage. The efficiency of the proposed modulation technique is verified by simulations of a four leg NPC inverter as an UPS feeding different types of dynamic and unbalanced loads.
Evaluation of direct attacks to fingerprint verification systems
(2011)

The vulnerabilities of fingerprint-based recognition systems to direct attacks with and without the cooperation of the user are studied. Two different systems, one minutiae-based and one ridge feature-based, are evaluated on a database of real and fake fingerprints. Based on the fingerprint images quality and on the results achieved on different operational scenarios, we obtain a number of statistically significant observations regarding the robustness of the systems.
Conjunctive representation of position, direction, and velocity in entorhinal cortex.
(2006)

Grid cells in the medial entorhinal cortex (MEC) are part of an environment-independent spatial coordinate system. To determine how information about location, direction, and distance is integrated in the grid-cell network, we recorded from each principal cell layer of MEC in rats that explored two-dimensional environments. Whereas layer II was predominated by grid cells, grid cells colocalized with head-direction cells and conjunctive grid x head-direction cells in the deeper layers. All cell types were modulated by running speed. The conjunction of positional, directional, and translational information in a single MEC cell type may enable grid coordinates to be updated during self-motion-based navigation.
Compliance through Informed Consent: Semantic Based Consent Permission and Data Management Model
(2017)

The General Data Protection Regulations (GDPR) imposes greater restrictions on obtaining valid user consents involving the use of personal data. A semantic model of consent can make the concepts of consent explicit, establish a common understanding and enable re-use of consent. Therefore, forming a semantic model of consent will satisfy the GDPR requirements of specificity and unambiguity and is an important step towards ensuring compliance. In this paper, we discuss obtaining an open vocabulary of expressing consent leveraging existing semantic models of provenance, processes, permission and obligations. We also present a reference architecture for the management of data processing according to consent permission. This data management model utilizes the open vocabulary of consent and incorporates the change of context into the data processing activity. By identifying and incorporating changes to the relational context between data controllers and data subjects into the data processing model, it aims to improve the integration of data management across different information systems specifically adhering to the GDPR and helping controllers to demonstrate compliance.
Enabling ECN in Multi-Service Multi-Queue Data Centers
(2016)

Recent proposals have leveraged Explicit Congestion Notification (ECN) to achieve high throughput low latency data center network (DCN) transport. However, most of them implicitly assume each switch port has one queue, making the ECN schemes they designed inapplicable to production DCNs where multiple service queues per port are employed to isolate different traffic classes through weighted fair sharing. In this paper, we reveal this problem by leveraging extensive testbed experiments to explore the intrinsic tradeoffs between throughput, latency, and weighted fair sharing in multi-queue scenarios. Using the guideline learned from the exploration, we design MQ-ECN, a simple yet effective solution to enable ECN for multi-service multiqueue production DCNs. Through a series of testbed experiments and large-scale simulations, we show that MQ-ECN breaks the tradeoffs by delivering both high throughput and low latency simultaneously, while still preserving weighted fair sharing.
Quantization Mimic: Towards Very Tiny CNN for Object Detection
(2018)

In this paper, we propose a simple and general framework for training very tiny CNNs (e.g. VGG with the number of channels reduced to 1 32 ) for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks. We utilize two types of acceleration methods: mimic and quantization. Mimic improves the performance of a student network by transfering knowledge from a teacher network. Quantization converts a full-precision network to a quantized one without large degradation of performance. If the teacher network is quantized, the search scope of the student network will be smaller. Using this feature of the quantization, we propose Quantization Mimic. It first quantizes the large network, then mimic a quantized small network. The quantization operation can help student network to better match the feature maps from teacher network. To evaluate our approach, we carry out experiments on various popular CNNs including VGG and Resnet, as well as different detection frameworks including Faster R-CNN and R-FCN. Experiments on Pascal VOC and WIDER FACE verify that our Quantization Mimic algorithm can be applied on various settings and outperforms state-of-the-art model acceleration methods given limited computing resouces.
Data Approximation Using Biarcs
(2002)

. An algorithm for data approximation with biarcs is presented. The method uses a specific formulation of biarcs appropriate for parametric curves in Bézier or NURBS formulation. A base curve is applied to obtain tangents and anchor points for the individual arcs joining in G 1 continuity. Data sampled from circular arcs or straight line segments is represented precisely by one biarc. The method is most useful in numerical control to drive the cutter along straight line or circular paths.
Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs
(2018)

SimpleQuestions is a commonly used benchmark for single-factoid question answering (QA) over Knowledge Graphs (KG). Existing QA systems rely on various components to solve different sub-tasks of the problem (such as entity detection, entity linking, relation prediction and evidence integration). In this work, we propose a different approach to the problem and present an information retrieval style solution for it. We adopt a two-phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. Our approach achieves an accuracy of 80% which sets a new state-of-the-art on the SimpleQuestions dataset.
Hypervisor Memory Forensics
(2013)

Memory forensics is the branch of computer forensics that aims at extracting artifacts from memory snapshots taken from a running system. Even though it is a relatively recent field, it is rapidly growing and it is attracting considerable attention from both industrial and academic researchers. In this paper, we present a set of techniques to extend the field of memory forensics toward the analysis of hypervisors and virtual machines. With the increasing adoption of virtualization techniques (both as part of the cloud and in normal desktop environments), we believe that memory forensics will soon play a very important role in many investigations that involve virtual environments. Our approach, implemented in an open source tool as an extension of the Volatility framework, is designed to detect both the existence and the characteristics of any hypervisor that uses the Intel VT-x technology. It also supports the analysis of nested virtualization and it is able to infer the hierarchy of multiple hypervisors and virtual machines. Finally, by exploiting the techniques presented in this paper, our tool can reconstruct the address space of a virtual machine in order to transparently support any existing Volatility plugin allowing analysts to reuse their code for the analysis of virtual environments.
A Scalable SIEM Correlation Engine and Its Application to the Olympic Games IT Infrastructure
(2013)

The security event correlation scalability has become a major concern for security analysts and IT administrators when considering complex IT infrastructures that need to handle gargantuan amounts of events or wide correlation window spans. The current correlation capabilities of Security Information and Event Management (SIEM), based on a single node in centralized servers, have proved to be insufficient to process large event streams. This paper introduces a step forward in the current state of the art to address the aforementioned problems. The proposed model takes into account the two main aspects of this field: distributed correlation and query parallelization. We present a case study of a multiple-step attack on the Olympic Games IT infrastructure to illustrate the applicability of our approach.
Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments
(2016)

Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.
Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations
(2018)

Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia. The short texts have a limited contextual information, and they are sparse, noisy and ambiguous, and hence, automatically learning topics from them remains an important challenge. To tackle this problem, in this paper, we propose a semantics-assisted non-negative matrix factorization (SeaNMF) model to discover topics for the short texts. It effectively incorporates the word-context semantic correlations into the model, where the semantic relationships between the words and their contexts are learned from the skip-gram view of the corpus. The SeaNMF model is solved using a block coordinate descent algorithm. We also develop a sparse variant of the SeaNMF model which can achieve a better model interpretability. Extensive quantitative evaluations on various realworld short text datasets demonstrate the superior performance of the proposed models over several other state-of-the-art methods in terms of topic coherence and classification accuracy. The qualitative semantic analysis demonstrates the interpretability of our models by discovering meaningful and consistent topics. With a simple formulation and the superior performance, SeaNMF can be an effective standard topic model for short texts.
Empirical research on consumers' initial trust and gender differences in B2C e-business
(2014)

Based on the Technology Acceptance Model and related literatures, this paper proposes an integrated conceptual model of consumers' initial trust in B2C e-business. Empirical research results show that, perceived usefulness, perceived safety, perceived website reputation and propensity to trust significantly positively affect consumers' initial trust in e-vendor, which further positively influence consumers' intention to purchase online, by the complete mediation of attitude; however, the effect of perceived use of use on initial trust doesn't show the statistic significance. Furthermore, there are significant gender differences in the effects of perceived ease of use, perceived safety, perceived website reputation on consumers' initial trust.
ACCIDENT AVOIDANCE AND DETECTION ON HIGHWAYS
(2012)

Technological approaches for detecting and monitoring fatigue levels of driver fatigue continue to emerge and many are now in the development, validation testing, or early implementation stages. Previous studies have reviewed available fatigue detection and prediction technologies and methodologies. As the name indicates this project is about advanced technologies in cars for making it more intelligent and interactive for avoiding accidents on roads. By using ARM7 this system becomes more efficient, reliable & effective. There are very less number of systems implemented on human behaviour detection in or with cars. In this paper, we describe a real-time online safety prototype that controls the vehicle speed under driver fatigue. The purpose of such a model is to advance a system to detect fatigue symptoms in drivers and control the speed of vehicle to avoid accidents. The main components of the system consist of number of real time sensors like gas, eye blink, alcohol, fuel, impact sensors and a software interface with GPS and Google Maps APIs for location.
A cryptosystem based on Vigenère cipher with varying key
(2012)

— Privacy is one of the key issues information Security addresses. Through encryption one can prevent a third party from understanding raw data during signal transmission. The encryption methods for enhancing the security of digital contents has gained high significance in the current era of breach of security and misuse of the confidential information intercepted and misused by the unauthorized parties. This paper sets out to contribute to the general body of knowledge in the area of cryptography application and by developing a new way of implementing Vigenère cipher encryption algorithm by automatically changing the cipher key after each encryption step. The new method will use successive keys that will be dependent on the initial key value during the encryption process. The algorithm ultimately makes it possible for encryption and decryption of the text. The ciphertext will have different encryption key pattern and the Vigenère cryptosystem will be more difficult to decipher using frequency attack. The implementation will be done using java programming.
Learning to recommend with explicit and implicit social relations
(2011)

Recommender systems have been well studied and developed, both in academia and in industry recently. However, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real-world observations where we always turn to our trusted friends for recommendations. Aiming at modeling recommender systems more accurately and realistically, we propose a novel probabilistic factor analysis framework which naturally fuses the users' tastes and their trusted friends' favors together. The proposed framework is quite general, and it can also be applied to pure user-item rating matrix even if we do not have explicit social trust information among users. In this framework, we coin the term social trust ensemble to represent the formulation of the social trust restrictions on the recommender systems. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results show that our method outperforms state-of-the-art approaches.
Activity Monitoring: Noticing Interesting Changes in Behavior
(1999)

We introduce a problem class which we term activity monitoring. Such problems involve monitoring the behavior of a large population of entities for interesting events requiring action. We present a framework within which each of the individual problems has a natural expression, as well as a methodology for evaluating performance of activity monitoring techniques. We show that two superficially different tasks, news story monitoring and intrusion detection, can be expressed naturally within the framework, and show that key differences in solution methods can be compared.